{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Obs: Converter em um arquivo .py para rodar.",
   "id": "6074df08f3e7986e"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Importações",
   "id": "b8211b6fd209c236"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:33.378123Z",
     "start_time": "2025-07-27T21:15:33.369615Z"
    }
   },
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<dask.config.set at 0x7f933d6c1e50>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 24,
   "source": [
    "import abc\n",
    "\n",
    "import argparse\n",
    "import os\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import warnings\n",
    "\n",
    "from keras.src.layers import LSTM, Dense\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "from pyESN import ESN\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from keras import Sequential, Input\n",
    "from pyswarms.single import GlobalBestPSO\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "\n",
    "from anneal import Annealer\n",
    "\n",
    "MODELOS = [\"ESN\", \"LSTM\", \"MLP\", \"RF\", \"XGBoost\"]\n",
    "OTIMIZADORES = [\"PSO\", \"SA\"]\n",
    "N_ITER = 15\n",
    "N_SOLUCOES = 15\n",
    "SEED = 100\n",
    "SEEDS = [1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000]\n",
    "CAMPUS_TREINO = \"PARANAGUÁ\"\n",
    "K_FOLDS = 5\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "def reset_seed(rnd_seed=SEED):\n",
    "    os.environ['PYTHONHASHSEED'] = '0'\n",
    "    random.seed(rnd_seed)\n",
    "    np.random.seed(rnd_seed)\n",
    "    tf.random.set_seed(rnd_seed)\n",
    "\n",
    "\n",
    "reset_seed()\n"
   ],
   "id": "bf4bde7a4e807b6b"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# Configuração dos Otimizadores\n",
    "## Otimizador Base"
   ],
   "id": "93062c1ee2643ff0"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:33.438071Z",
     "start_time": "2025-07-27T21:15:33.429323Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class Otimizador:\n",
    "\n",
    "    @abc.abstractmethod\n",
    "    def __init__(self, dataset, features, n_solucoes, n_iteracoes, seed=SEED):\n",
    "        reset_seed(seed)\n",
    "        self.nome_modelo = None\n",
    "        self.dataset = dataset\n",
    "        self.features = features\n",
    "        self.seed = seed\n",
    "        self.n_solucoes = n_solucoes\n",
    "        self.n_iteracoes = n_iteracoes\n",
    "        self.solucoes = []\n",
    "        self.iteracoes = []\n",
    "\n",
    "    @abc.abstractmethod\n",
    "    def run(self):\n",
    "        pass\n",
    "\n",
    "    @abc.abstractmethod\n",
    "    def run_objective_function(self, parametros):\n",
    "        pass\n",
    "\n",
    "    def objective_function(self, modelo, parametros):\n",
    "        dataset_treino = self.dataset[self.dataset[\"CAMPUS\"] == CAMPUS_TREINO].copy()\n",
    "        cvs = []\n",
    "        for i_treino, i_teste in TimeSeriesSplit(n_splits=K_FOLDS, test_size=1).split(dataset_treino):\n",
    "            # Treinamento com apenas o campus com a maior quantidade de dados\n",
    "            x_treino = dataset_treino[self.features].iloc[i_treino].astype(np.float32).to_numpy()\n",
    "            y_treino = dataset_treino[\"CLASSE\"].iloc[i_treino].to_numpy()\n",
    "\n",
    "            # Teste com os dados de todos os campus\n",
    "            data_teste = dataset_treino.iloc[i_teste][\"DATA\"].values[0]\n",
    "            df_teste = self.dataset.loc[self.dataset[\"DATA\"] == data_teste]\n",
    "            x_teste = df_teste[self.features].astype(np.float32).to_numpy()\n",
    "            y_teste = df_teste[\"CLASSE\"].to_numpy()\n",
    "\n",
    "            if (isinstance(modelo, RandomForestClassifier)\n",
    "                    or isinstance(modelo, XGBClassifier)\n",
    "                    or isinstance(modelo, MLPClassifier)):\n",
    "                modelo.fit(x_treino, y_treino)\n",
    "                y_previsto = modelo.predict(x_teste)\n",
    "            elif isinstance(modelo, ESN):\n",
    "                modelo.fit(x_treino, OneHotEncoder(sparse_output=False).fit_transform(y_treino.reshape(-1, 1)))\n",
    "                y_previsto = [np.argmax(val) for val in modelo.predict(x_teste)]\n",
    "            else:\n",
    "                modelo.fit(x_treino, y_treino, shuffle=False, verbose=0, epochs=parametros.epochs,\n",
    "                           batch_size=parametros.batch_size)\n",
    "                y_previsto = [np.argmax(val) for val in modelo.predict(x_teste, verbose=0)]\n",
    "\n",
    "            cvs.append(accuracy_score(y_teste, y_previsto))\n",
    "\n",
    "        return np.array(cvs).mean() * - 1\n",
    "\n",
    "    def iteracoes_dataframe(self):\n",
    "        df = pd.DataFrame()\n",
    "        for i in range(len(self.iteracoes)):\n",
    "            part = self.iteracoes[i]\n",
    "            df = pd.concat([df, pd.DataFrame.from_dict(part.to_dict(), orient='index').T], ignore_index=True)\n",
    "        return df\n",
    "\n",
    "    def salvar_csv(self):\n",
    "        pd_df = self.iteracoes_dataframe()\n",
    "        pd_df.to_csv(f\"resultados/otimização - classificação/{self.nome_modelo} SEED {self.seed}.csv\", sep=\";\",\n",
    "                     decimal=\".\",\n",
    "                     index=True)\n"
   ],
   "id": "4b250006f07db26d",
   "outputs": [],
   "execution_count": 25
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### ESN",
   "id": "200ce2d21089e5bb"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:33.549293Z",
     "start_time": "2025-07-27T21:15:33.544342Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class SolucaoESN:\n",
    "    def __init__(self):\n",
    "        self.fitness = None\n",
    "        self.n_reservoirs = 0\n",
    "        self.sparsity = 0\n",
    "        self.spectral_radius = 0\n",
    "\n",
    "    def to_dict(self):\n",
    "        return {\n",
    "            \"Reservoirs\": self.n_reservoirs,\n",
    "            \"Sparsity\": self.sparsity,\n",
    "            \"Spectral Radius\": self.spectral_radius,\n",
    "            \"Fitness\": self.fitness,\n",
    "        }"
   ],
   "id": "6901a15ce2ff59dd",
   "outputs": [],
   "execution_count": 26
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### LSTM",
   "id": "be2022de9d800667"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:33.587632Z",
     "start_time": "2025-07-27T21:15:33.560607Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class SolucaoLSTM:\n",
    "    def __init_(self):\n",
    "        self.fitness = None\n",
    "        self.lstm_units = 0\n",
    "        self.epochs = 0\n",
    "        self.batch_size = 0\n",
    "        self.lstm_activation = None\n",
    "\n",
    "    def to_dict(self):\n",
    "        return {\n",
    "            \"Units\": self.lstm_units,\n",
    "            \"Epochs\": self.epochs,\n",
    "            \"Batch Size\": self.batch_size,\n",
    "            \"Activation\": self.lstm_activation,\n",
    "            \"Fitness\": self.fitness,\n",
    "        }\n"
   ],
   "id": "fbb40426734e86bd",
   "outputs": [],
   "execution_count": 27
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### MLP",
   "id": "19205939e8896fe1"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:33.646592Z",
     "start_time": "2025-07-27T21:15:33.641922Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class SolucaoMLP:\n",
    "    def __init_(self):\n",
    "        self.fitness = None\n",
    "        self.hidden_layer_sizes = 0\n",
    "        self.alpha = 0\n",
    "        self.activation = None\n",
    "\n",
    "    def to_dict(self):\n",
    "        return {\n",
    "            \"Hidden Layers\": self.hidden_layer_sizes,\n",
    "            \"Alpha\": self.alpha,\n",
    "            \"Activation\": self.activation,\n",
    "            \"Fitness\": self.fitness,\n",
    "        }\n"
   ],
   "id": "b17b26036c9e16a7",
   "outputs": [],
   "execution_count": 28
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Random Forest",
   "id": "5877f1f5c38e74f6"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:33.712013Z",
     "start_time": "2025-07-27T21:15:33.708824Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class SolucaoRF:\n",
    "    def __init_(self):\n",
    "        self.fitness = None\n",
    "        self.estimators = 0\n",
    "        self.max_depth = 0\n",
    "        self.min_samples_split = 0\n",
    "        self.min_samples_leaf = 0\n",
    "\n",
    "    def to_dict(self):\n",
    "        return {\n",
    "            \"N_estimators\": self.estimators,\n",
    "            \"Max_depth\": self.max_depth,\n",
    "            \"Min_samples_split\": self.min_samples_split,\n",
    "            \"Min_samples_leaf\": self.min_samples_leaf,\n",
    "            \"Fitness\": self.fitness,\n",
    "        }"
   ],
   "id": "d6eb2a4060a6bbcc",
   "outputs": [],
   "execution_count": 29
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### XGBoost",
   "id": "70c86d802a6db946"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:33.779887Z",
     "start_time": "2025-07-27T21:15:33.775586Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class SolucaoXGB:\n",
    "    def __init_(self):\n",
    "        self.fitness = None\n",
    "        self.estimators = 0\n",
    "        self.max_depth = 0\n",
    "        self.booster = None\n",
    "        self.reg_lambda = 0\n",
    "        self.reg_alpha = 0\n",
    "\n",
    "    def to_dict(self):\n",
    "        return {\n",
    "            \"N_estimators\": self.estimators,\n",
    "            \"Max_depth\": self.max_depth,\n",
    "            \"Booster\": self.booster,\n",
    "            \"Lambda\": self.reg_lambda,\n",
    "            \"Alpha\": self.reg_alpha,\n",
    "            \"Fitness\": self.fitness,\n",
    "        }\n"
   ],
   "id": "cbb3af390d0ff686",
   "outputs": [],
   "execution_count": 30
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Simulated Annealing (SA)",
   "id": "416a8594d3eb7720"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:33.834464Z",
     "start_time": "2025-07-27T21:15:33.828181Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class SimulatedAnneal(Annealer):\n",
    "    def __init__(self, params, objective_function, max_iter):\n",
    "        self.params = params\n",
    "        self.objective_function = objective_function\n",
    "        super().__init__(self.random_initial_state())\n",
    "        self.steps = max_iter\n",
    "        self.Tmin = 0.0001\n",
    "        self.Tmax = 1\n",
    "        self.updates = 0\n",
    "        self.anneal()\n",
    "\n",
    "    def random_initial_state(self):\n",
    "        initial_state = {}\n",
    "        for key in self.params.keys():\n",
    "            initial_state[key] = random.choice(self.params[key])\n",
    "        return initial_state\n",
    "\n",
    "    def move(self):\n",
    "        atual = self.state\n",
    "        for key in self.params.keys():\n",
    "            valor = atual[key]\n",
    "            opcoes = len(self.params[key])\n",
    "            if opcoes <= 2:\n",
    "                self.state[key] = random.choice(self.params[key])\n",
    "            else:\n",
    "                intervalo = int(np.round(self.T * opcoes))\n",
    "                diferencas = np.abs(np.array(self.params[key]) - valor)\n",
    "                index = np.argmin(diferencas)\n",
    "                inicio = max(0, index - intervalo)\n",
    "                fim = min(opcoes - 1, index + intervalo)\n",
    "                if inicio == fim:\n",
    "                    self.state[key] = self.params[key][inicio]\n",
    "                else:\n",
    "                    self.state[key] = self.params[key][random.choice(range(inicio, fim))]\n",
    "\n",
    "    def energy(self):\n",
    "        return self.objective_function(self.state)"
   ],
   "id": "2cdfa5f8471f390b",
   "outputs": [],
   "execution_count": 31
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### ESN",
   "id": "323847293a286d4d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:33.903711Z",
     "start_time": "2025-07-27T21:15:33.897585Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class SAESN(Otimizador):\n",
    "\n",
    "    def __init__(self, dataset, features, n_solucoes, n_iteracoes, seed=SEED):\n",
    "        super().__init__(dataset, features, n_solucoes, n_iteracoes, seed)\n",
    "        self.nome_modelo = \"SA-ESN\"\n",
    "        self.run()\n",
    "\n",
    "    def run(self):\n",
    "        parametros = {\n",
    "            \"n_reservoirs\": range(10, 400),\n",
    "            \"sparsity\": np.arange(0.001, 0.8, 0.001),\n",
    "            \"spectral_radius\": np.arange(0.001, 0.8, 0.001),\n",
    "        }\n",
    "        SimulatedAnneal(parametros, self.run_objective_function, max_iter=self.n_iteracoes * self.n_solucoes)\n",
    "\n",
    "    def run_objective_function(self, parametros):\n",
    "        solucao = SolucaoESN()\n",
    "        solucao.n_reservoirs = int(parametros[\"n_reservoirs\"])\n",
    "        solucao.sparsity = np.round(parametros[\"sparsity\"], 4)\n",
    "        solucao.spectral_radius = np.round(parametros[\"spectral_radius\"], 4)\n",
    "\n",
    "        search = list(filter(lambda par:\n",
    "                             par.n_reservoirs == solucao.n_reservoirs and\n",
    "                             par.sparsity == solucao.sparsity and\n",
    "                             par.spectral_radius == solucao.spectral_radius, self.solucoes))\n",
    "\n",
    "        if search:\n",
    "            solucao = search[0]\n",
    "\n",
    "        else:\n",
    "            modelo = ESN(n_inputs=self.dataset[self.features].shape[1],\n",
    "                         n_outputs=self.dataset[\"CLASSE\"].nunique(),\n",
    "                         n_reservoir=solucao.n_reservoirs,\n",
    "                         sparsity=solucao.sparsity,\n",
    "                         spectral_radius=solucao.spectral_radius,\n",
    "                         random_state=self.seed)\n",
    "\n",
    "            solucao.fitness = self.objective_function(modelo, solucao)\n",
    "\n",
    "        self.solucoes.append(solucao)\n",
    "\n",
    "        self.solucoes = sorted(self.solucoes, key=lambda a: a.fitness)\n",
    "        best = self.solucoes[0]\n",
    "        self.iteracoes.append(best)\n",
    "        self.salvar_csv()\n",
    "\n",
    "        return solucao.fitness"
   ],
   "id": "99ab52423b5f2e8b",
   "outputs": [],
   "execution_count": 32
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### LSTM",
   "id": "849a45d05bd77ec"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:33.962063Z",
     "start_time": "2025-07-27T21:15:33.955157Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class SALSTM(Otimizador):\n",
    "    def __init__(self, dataset, features, n_solucoes, n_iteracoes, seed=SEED):\n",
    "        super().__init__(dataset, features, n_solucoes, n_iteracoes, seed)\n",
    "        self.nome_modelo = \"SA-LSTM\"\n",
    "        self.ACTIVATIONS = [\"linear\", \"mish\", \"sigmoid\", \"softmax\", \"softplus\", \"softsign\", \"tanh\"]\n",
    "        self.run()\n",
    "\n",
    "    def run(self):\n",
    "        parametros = {\n",
    "            \"lstm_units\": range(10, 400),\n",
    "            \"epochs\": range(10, 400),\n",
    "            \"batch_size\": range(10, 400),\n",
    "            \"lstm_activation\": range(0, 7),\n",
    "        }\n",
    "        SimulatedAnneal(parametros, self.run_objective_function, max_iter=self.n_iteracoes * self.n_solucoes)\n",
    "\n",
    "    def run_objective_function(self, parametros):\n",
    "        solucao = SolucaoLSTM()\n",
    "        solucao.lstm_units = int(parametros[\"lstm_units\"])\n",
    "        solucao.epochs = int(parametros[\"epochs\"])\n",
    "        solucao.batch_size = int(parametros[\"batch_size\"])\n",
    "        solucao.lstm_activation = self.ACTIVATIONS[int(parametros[\"lstm_activation\"])]\n",
    "\n",
    "        search = list(filter(lambda par:\n",
    "                             par.lstm_units == solucao.lstm_units and\n",
    "                             par.epochs == solucao.epochs and\n",
    "                             par.batch_size == solucao.batch_size and\n",
    "                             par.lstm_activation == solucao.lstm_activation, self.solucoes))\n",
    "\n",
    "        if search:\n",
    "            solucao = search[0]\n",
    "\n",
    "        else:\n",
    "            tf.keras.backend.clear_session()\n",
    "            modelo = Sequential([\n",
    "                Input((self.dataset[self.features].shape[1], 1)),\n",
    "                LSTM(solucao.lstm_units,\n",
    "                     activation=solucao.lstm_activation,\n",
    "                     seed=self.seed),\n",
    "                Dense(self.dataset[\"CLASSE\"].nunique(), activation=\"softmax\"),\n",
    "            ])\n",
    "            modelo.compile(loss='sparse_categorical_crossentropy',\n",
    "                           metrics=['accuracy'])\n",
    "\n",
    "            solucao.fitness = self.objective_function(modelo, solucao)\n",
    "\n",
    "        self.solucoes.append(solucao)\n",
    "\n",
    "        self.solucoes = sorted(self.solucoes, key=lambda a: a.fitness)\n",
    "        best = self.solucoes[0]\n",
    "        self.iteracoes.append(best)\n",
    "        self.salvar_csv()\n",
    "\n",
    "        return solucao.fitness"
   ],
   "id": "a30d2a89c21d6991",
   "outputs": [],
   "execution_count": 33
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### MLP",
   "id": "d059fc93087ecbf5"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:34.015895Z",
     "start_time": "2025-07-27T21:15:34.009304Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class SAMLP(Otimizador):\n",
    "    def __init__(self, dataset, features, n_solucoes, n_iteracoes, seed=SEED):\n",
    "        super().__init__(dataset, features, n_solucoes, n_iteracoes, seed)\n",
    "        self.nome_modelo = \"SA-MLP\"\n",
    "        self.ACTIVATIONS = [\"identity\", \"logistic\", \"tanh\", \"relu\"]\n",
    "        self.run()\n",
    "\n",
    "    def run(self):\n",
    "        parametros = {\n",
    "            \"hidden_layer_sizes\": range(10, 400),\n",
    "            \"alpha\": np.arange(0, 1, 0.01),\n",
    "            \"activation\": range(0, 4),\n",
    "        }\n",
    "        SimulatedAnneal(parametros, self.run_objective_function, max_iter=self.n_iteracoes * self.n_solucoes)\n",
    "\n",
    "    def run_objective_function(self, parametros):\n",
    "        solucao = SolucaoMLP()\n",
    "        solucao.hidden_layer_sizes = int(parametros[\"hidden_layer_sizes\"])\n",
    "        solucao.alpha = round(parametros[\"alpha\"], 4)\n",
    "        solucao.activation = self.ACTIVATIONS[int(parametros[\"activation\"])]\n",
    "\n",
    "        search = list(filter(lambda par:\n",
    "                             par.hidden_layer_sizes == solucao.hidden_layer_sizes and\n",
    "                             par.alpha == solucao.alpha and\n",
    "                             par.activation == solucao.activation, self.solucoes))\n",
    "\n",
    "        if search:\n",
    "            solucao = search[0]\n",
    "\n",
    "        else:\n",
    "            modelo = MLPClassifier(hidden_layer_sizes=(solucao.hidden_layer_sizes,),\n",
    "                                   activation=solucao.activation,\n",
    "                                   alpha=solucao.alpha,\n",
    "                                   random_state=self.seed)\n",
    "\n",
    "            solucao.fitness = self.objective_function(modelo, solucao)\n",
    "\n",
    "        self.solucoes.append(solucao)\n",
    "\n",
    "        self.solucoes = sorted(self.solucoes, key=lambda a: a.fitness)\n",
    "        best = self.solucoes[0]\n",
    "        self.iteracoes.append(best)\n",
    "        self.salvar_csv()\n",
    "\n",
    "        return solucao.fitness"
   ],
   "id": "8b74920a54a69862",
   "outputs": [],
   "execution_count": 34
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Random Forest\n",
   "id": "259dbd8726b34254"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:34.117845Z",
     "start_time": "2025-07-27T21:15:34.112210Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class SARF(Otimizador):\n",
    "    def __init__(self, dataset, features, n_solucoes, n_iteracoes, seed=SEED):\n",
    "        super().__init__(dataset, features, n_solucoes, n_iteracoes, seed)\n",
    "        self.nome_modelo = \"SA-RF\"\n",
    "        self.run()\n",
    "\n",
    "    def run(self):\n",
    "        parametros = {\n",
    "            \"estimators\": range(10, 400),\n",
    "            \"max_depth\": range(10, 400),\n",
    "            \"min_samples_split\": range(2, 50),\n",
    "            \"min_samples_leaf\": range(2, 50),\n",
    "        }\n",
    "        SimulatedAnneal(parametros, self.run_objective_function, max_iter=self.n_iteracoes * self.n_solucoes)\n",
    "\n",
    "    def run_objective_function(self, parametros):\n",
    "        solucao = SolucaoRF()\n",
    "        solucao.estimators = int(parametros[\"estimators\"])\n",
    "        solucao.max_depth = int(parametros[\"max_depth\"])\n",
    "        solucao.min_samples_split = int(parametros[\"min_samples_split\"])\n",
    "        solucao.min_samples_leaf = int(parametros[\"min_samples_leaf\"])\n",
    "\n",
    "        search = list(filter(lambda par:\n",
    "                             par.estimators == solucao.estimators and\n",
    "                             par.max_depth == solucao.max_depth and\n",
    "                             par.min_samples_split == solucao.min_samples_split and\n",
    "                             par.min_samples_leaf == solucao.min_samples_leaf, self.solucoes))\n",
    "\n",
    "        if search:\n",
    "            solucao = search[0]\n",
    "\n",
    "        else:\n",
    "            modelo = RandomForestClassifier(random_state=self.seed,\n",
    "                                            n_estimators=solucao.estimators,\n",
    "                                            max_depth=solucao.max_depth,\n",
    "                                            min_samples_split=solucao.min_samples_split,\n",
    "                                            min_samples_leaf=solucao.min_samples_leaf)\n",
    "\n",
    "            solucao.fitness = self.objective_function(modelo, solucao)\n",
    "\n",
    "        self.solucoes.append(solucao)\n",
    "\n",
    "        self.solucoes = sorted(self.solucoes, key=lambda a: a.fitness)\n",
    "        best = self.solucoes[0]\n",
    "        self.iteracoes.append(best)\n",
    "        self.salvar_csv()\n",
    "\n",
    "        return solucao.fitness\n"
   ],
   "id": "bbf67ddb94e4152",
   "outputs": [],
   "execution_count": 35
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### XGBoost",
   "id": "a607b504601612c1"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:34.171117Z",
     "start_time": "2025-07-27T21:15:34.157706Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class SAXGB(Otimizador):\n",
    "    def __init__(self, dataset, features, n_solucoes, n_iteracoes, seed=SEED):\n",
    "        super().__init__(dataset, features, n_solucoes, n_iteracoes, seed)\n",
    "        self.nome_modelo = \"SA-XGBoost\"\n",
    "        self.BOOSTERS = [\"gbtree\", \"gblinear\", \"dart\"]\n",
    "        self.run()\n",
    "\n",
    "    def run(self):\n",
    "        parametros = {\n",
    "            \"estimators\": range(10, 400),\n",
    "            \"max_depth\": range(10, 400),\n",
    "            \"booster\": range(0, 2),\n",
    "            \"reg_lambda\": np.arange(0, 1, 0.005),\n",
    "            \"reg_alpha\": np.arange(0, 1, 0.005),\n",
    "        }\n",
    "        SimulatedAnneal(parametros, self.run_objective_function, max_iter=self.n_iteracoes * self.n_solucoes)\n",
    "\n",
    "    def run_objective_function(self, parametros):\n",
    "        solucao = SolucaoXGB()\n",
    "        solucao.estimators = int(parametros[\"estimators\"])\n",
    "        solucao.max_depth = int(parametros[\"max_depth\"])\n",
    "        solucao.booster = self.BOOSTERS[int(parametros[\"booster\"])]\n",
    "        solucao.reg_lambda = float(parametros[\"reg_lambda\"])\n",
    "        solucao.reg_alpha = float(parametros[\"reg_alpha\"])\n",
    "\n",
    "        search = list(filter(lambda par:\n",
    "                             par.estimators == solucao.estimators and\n",
    "                             par.max_depth == solucao.max_depth and\n",
    "                             par.booster == solucao.booster and\n",
    "                             par.reg_lambda == solucao.reg_lambda and\n",
    "                             par.reg_alpha == solucao.reg_alpha, self.solucoes))\n",
    "\n",
    "        if search:\n",
    "            solucao = search[0]\n",
    "\n",
    "        else:\n",
    "            updater = \"coord_descent\" if solucao.booster == \"gblinear\" else None\n",
    "            modelo = XGBClassifier(random_state=self.seed,\n",
    "                                   n_estimators=solucao.estimators,\n",
    "                                   max_depth=solucao.max_depth,\n",
    "                                   booster=solucao.booster,\n",
    "                                   reg_lambda=solucao.reg_lambda,\n",
    "                                   reg_alpha=solucao.reg_alpha,\n",
    "                                   updater=updater)\n",
    "\n",
    "            solucao.fitness = self.objective_function(modelo, solucao)\n",
    "\n",
    "        self.solucoes.append(solucao)\n",
    "\n",
    "        self.solucoes = sorted(self.solucoes, key=lambda a: a.fitness)\n",
    "        best = self.solucoes[0]\n",
    "        self.iteracoes.append(best)\n",
    "        self.salvar_csv()\n",
    "\n",
    "        return solucao.fitness"
   ],
   "id": "23ce1460899b8e29",
   "outputs": [],
   "execution_count": 36
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Particle Swarm Optimization (PSO)",
   "id": "e3c1c719bcd9468a"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### ESN",
   "id": "85a8feb053ec3ae0"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:34.223662Z",
     "start_time": "2025-07-27T21:15:34.217102Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class PSOESN(Otimizador):\n",
    "    def __init__(self, dataset, features, n_solucoes, n_iteracoes, seed=SEED):\n",
    "        super().__init__(dataset, features, n_solucoes, n_iteracoes, seed)\n",
    "        self.nome_modelo = \"PSO-ESN\"\n",
    "        self.run()\n",
    "\n",
    "    def run(self):\n",
    "        lower_bound = [10, 0.001, 0.001]\n",
    "        uppper_bound = [400, 0.8, 0.8]\n",
    "        bounds = (lower_bound, uppper_bound)\n",
    "\n",
    "        options = {'c1': 0.5, 'c2': 0.5, 'w': 0.5}\n",
    "        optimizer = GlobalBestPSO(n_particles=self.n_solucoes,\n",
    "                                  dimensions=3,\n",
    "                                  options=options,\n",
    "                                  bounds=bounds)\n",
    "\n",
    "        optimizer.optimize(self.get_fitness, iters=self.n_iteracoes)\n",
    "\n",
    "    def get_fitness(self, parts):\n",
    "        fit_lst = [self.run_objective_function(parts[j]) for j in range(self.n_solucoes)]\n",
    "\n",
    "        self.solucoes = sorted(self.solucoes, key=lambda a: a.fitness)\n",
    "        best = self.solucoes[0]\n",
    "        self.iteracoes.append(best)\n",
    "        self.salvar_csv()\n",
    "\n",
    "        return fit_lst\n",
    "\n",
    "    def run_objective_function(self, particle_arr):\n",
    "        solucao = SolucaoESN()\n",
    "        solucao.n_reservoirs = int(particle_arr[0])\n",
    "        solucao.sparsity = round(particle_arr[1], 4)\n",
    "        solucao.spectral_radius = round(particle_arr[2], 4)\n",
    "\n",
    "        search = list(filter(lambda par:\n",
    "                             par.n_reservoirs == solucao.n_reservoirs and\n",
    "                             par.sparsity == solucao.sparsity and\n",
    "                             par.spectral_radius == solucao.spectral_radius, self.solucoes))\n",
    "\n",
    "        if search:\n",
    "            self.solucoes.append(search[0])\n",
    "            return search[0].fitness\n",
    "\n",
    "        modelo = ESN(n_inputs=self.dataset[self.features].shape[1],\n",
    "                     n_outputs=self.dataset[\"CLASSE\"].nunique(),\n",
    "                     n_reservoir=solucao.n_reservoirs,\n",
    "                     sparsity=solucao.sparsity,\n",
    "                     spectral_radius=solucao.spectral_radius,\n",
    "                     random_state=self.seed)\n",
    "\n",
    "        solucao.fitness = self.objective_function(modelo, solucao)\n",
    "\n",
    "        self.solucoes.append(solucao)\n",
    "\n",
    "        return solucao.fitness"
   ],
   "id": "a0cbac5552dd6983",
   "outputs": [],
   "execution_count": 37
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "\n",
    "### LSTM"
   ],
   "id": "d4cce656e19e673a"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:34.274099Z",
     "start_time": "2025-07-27T21:15:34.267207Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class PSOLSTM(Otimizador):\n",
    "    def __init__(self, dataset, features, n_solucoes, n_iteracoes, seed=SEED):\n",
    "        super().__init__(dataset, features, n_solucoes, n_iteracoes, seed)\n",
    "        self.nome_modelo = \"PSO-LSTM\"\n",
    "        self.ACTIVATIONS = [\"linear\", \"mish\", \"sigmoid\", \"softmax\", \"softplus\", \"softsign\", \"tanh\"]\n",
    "        self.run()\n",
    "\n",
    "    def run(self):\n",
    "        lower_bound = [10, 10, 10, 0]\n",
    "        uppper_bound = [400, 400, 400, 7]\n",
    "        bounds = (lower_bound, uppper_bound)\n",
    "\n",
    "        options = {'c1': 0.5, 'c2': 0.5, 'w': 0.5}\n",
    "        optimizer = GlobalBestPSO(n_particles=self.n_solucoes,\n",
    "                                  dimensions=4,\n",
    "                                  options=options,\n",
    "                                  bounds=bounds)\n",
    "\n",
    "        optimizer.optimize(self.get_fitness, iters=self.n_iteracoes)\n",
    "\n",
    "    def get_fitness(self, parts):\n",
    "        fit_lst = [self.run_objective_function(parts[j]) for j in range(self.n_solucoes)]\n",
    "\n",
    "        self.solucoes = sorted(self.solucoes, key=lambda a: a.fitness)\n",
    "        best = self.solucoes[0]\n",
    "        self.iteracoes.append(best)\n",
    "        self.salvar_csv()\n",
    "\n",
    "        return fit_lst\n",
    "\n",
    "    def run_objective_function(self, particle_arr):\n",
    "        solucao = SolucaoLSTM()\n",
    "        solucao.lstm_units = int(particle_arr[0])\n",
    "        solucao.epochs = int(particle_arr[1])\n",
    "        solucao.batch_size = int(particle_arr[2])\n",
    "        solucao.lstm_activation = self.ACTIVATIONS[int(particle_arr[3])]\n",
    "\n",
    "        search = list(filter(lambda par:\n",
    "                             par.lstm_units == solucao.lstm_units and\n",
    "                             par.epochs == solucao.epochs and\n",
    "                             par.batch_size == solucao.batch_size and\n",
    "                             par.lstm_activation == solucao.lstm_activation, self.solucoes))\n",
    "\n",
    "        if search:\n",
    "            self.solucoes.append(search[0])\n",
    "            return search[0].fitness\n",
    "        tf.keras.backend.clear_session()\n",
    "        modelo = Sequential([\n",
    "            Input((self.dataset[self.features].shape[1], 1)),\n",
    "            LSTM(solucao.lstm_units,\n",
    "                 activation=solucao.lstm_activation,\n",
    "                 seed=self.seed),\n",
    "            Dense(self.dataset[\"CLASSE\"].nunique(), activation=\"softmax\"),\n",
    "        ])\n",
    "        modelo.compile(loss='sparse_categorical_crossentropy',\n",
    "                       metrics=['accuracy'])\n",
    "\n",
    "        solucao.fitness = self.objective_function(modelo, solucao)\n",
    "\n",
    "        self.solucoes.append(solucao)\n",
    "\n",
    "        return solucao.fitness"
   ],
   "id": "6b84cb47c77d6a6e",
   "outputs": [],
   "execution_count": 38
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### MLP",
   "id": "1e177db033d3078e"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:34.326308Z",
     "start_time": "2025-07-27T21:15:34.320447Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class PSOMLP(Otimizador):\n",
    "    def __init__(self, dataset, features, n_solucoes, n_iteracoes, seed=SEED):\n",
    "        super().__init__(dataset, features, n_solucoes, n_iteracoes, seed)\n",
    "        self.nome_modelo = \"PSO-MLP\"\n",
    "        self.ACTIVATIONS = [\"identity\", \"logistic\", \"tanh\", \"relu\"]\n",
    "        self.run()\n",
    "\n",
    "    def run(self):\n",
    "\n",
    "        lower_bound = [10, 0, 0]\n",
    "        uppper_bound = [400, 1.0, 4]\n",
    "        bounds = (lower_bound, uppper_bound)\n",
    "\n",
    "        options = {'c1': 0.5, 'c2': 0.5, 'w': 0.5}\n",
    "        optimizer = GlobalBestPSO(n_particles=self.n_solucoes,\n",
    "                                  dimensions=3,\n",
    "                                  options=options,\n",
    "                                  bounds=bounds)\n",
    "\n",
    "        optimizer.optimize(self.get_fitness, iters=self.n_iteracoes)\n",
    "\n",
    "    def get_fitness(self, parts):\n",
    "        fit_lst = [self.run_objective_function(parts[j]) for j in range(self.n_solucoes)]\n",
    "\n",
    "        self.solucoes = sorted(self.solucoes, key=lambda a: a.fitness)\n",
    "        best = self.solucoes[0]\n",
    "        self.iteracoes.append(best)\n",
    "        self.salvar_csv()\n",
    "\n",
    "        return fit_lst\n",
    "\n",
    "    def run_objective_function(self, particle_arr):\n",
    "        solucao = SolucaoMLP()\n",
    "        solucao.hidden_layer_sizes = int(particle_arr[0])\n",
    "        solucao.alpha = particle_arr[1]\n",
    "        solucao.activation = self.ACTIVATIONS[int(particle_arr[2])]\n",
    "\n",
    "        search = list(filter(lambda par:\n",
    "                             par.hidden_layer_sizes == solucao.hidden_layer_sizes and\n",
    "                             par.alpha == solucao.alpha and\n",
    "                             par.activation == solucao.activation, self.solucoes))\n",
    "\n",
    "        if search:\n",
    "            self.solucoes.append(search[0])\n",
    "            return search[0].fitness\n",
    "\n",
    "        modelo = MLPClassifier(hidden_layer_sizes=(solucao.hidden_layer_sizes,),\n",
    "                               activation=solucao.activation,\n",
    "                               alpha=solucao.alpha,\n",
    "                               random_state=self.seed)\n",
    "\n",
    "        solucao.fitness = self.objective_function(modelo, solucao)\n",
    "\n",
    "        self.solucoes.append(solucao)\n",
    "\n",
    "        return solucao.fitness"
   ],
   "id": "9a330518c6b2ac24",
   "outputs": [],
   "execution_count": 39
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Random Forest\n",
   "id": "dbd6562e12586654"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:34.383624Z",
     "start_time": "2025-07-27T21:15:34.377426Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class PSORF(Otimizador):\n",
    "    def __init__(self, dataset, features, n_solucoes, n_iteracoes, seed=SEED):\n",
    "        super().__init__(dataset, features, n_solucoes, n_iteracoes, seed)\n",
    "        self.nome_modelo = \"PSO-RF\"\n",
    "        self.run()\n",
    "\n",
    "    def run(self):\n",
    "        lower_bound = [10, 10, 2, 2]\n",
    "        uppper_bound = [400, 400, 50, 50]\n",
    "        bounds = (lower_bound, uppper_bound)\n",
    "\n",
    "        options = {'c1': 0.5, 'c2': 0.5, 'w': 0.5}\n",
    "        optimizer = GlobalBestPSO(n_particles=self.n_solucoes,\n",
    "                                  dimensions=4,\n",
    "                                  options=options,\n",
    "                                  bounds=bounds)\n",
    "\n",
    "        optimizer.optimize(self.get_fitness, iters=self.n_iteracoes)\n",
    "\n",
    "    def get_fitness(self, parts):\n",
    "        fit_lst = [self.run_objective_function(parts[j]) for j in range(self.n_solucoes)]\n",
    "\n",
    "        self.solucoes = sorted(self.solucoes, key=lambda a: a.fitness)\n",
    "        best = self.solucoes[0]\n",
    "        self.iteracoes.append(best)\n",
    "        self.salvar_csv()\n",
    "\n",
    "        return fit_lst\n",
    "\n",
    "    def run_objective_function(self, particle_arr):\n",
    "        solucao = SolucaoRF()\n",
    "        solucao.estimators = int(particle_arr[0])\n",
    "        solucao.max_depth = int(particle_arr[1])\n",
    "        solucao.min_samples_split = int(particle_arr[2])\n",
    "        solucao.min_samples_leaf = int(particle_arr[3])\n",
    "\n",
    "        search = list(filter(lambda par:\n",
    "                             par.estimators == solucao.estimators and\n",
    "                             par.max_depth == solucao.max_depth and\n",
    "                             par.min_samples_split == solucao.min_samples_split and\n",
    "                             par.min_samples_leaf == solucao.min_samples_leaf, self.solucoes))\n",
    "\n",
    "        if search:\n",
    "            self.solucoes.append(search[0])\n",
    "            return search[0].fitness\n",
    "\n",
    "        modelo = RandomForestClassifier(random_state=self.seed,\n",
    "                                        n_estimators=solucao.estimators,\n",
    "                                        max_depth=solucao.max_depth,\n",
    "                                        min_samples_split=solucao.min_samples_split,\n",
    "                                        min_samples_leaf=solucao.min_samples_leaf)\n",
    "\n",
    "        solucao.fitness = self.objective_function(modelo, solucao)\n",
    "\n",
    "        self.solucoes.append(solucao)\n",
    "\n",
    "        return solucao.fitness\n"
   ],
   "id": "da948c71177c42de",
   "outputs": [],
   "execution_count": 40
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### XGBoost",
   "id": "c26d092119fdb0e1"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:34.444388Z",
     "start_time": "2025-07-27T21:15:34.437408Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class PSOXGB(Otimizador):\n",
    "    def __init__(self, dataset, features, n_solucoes, n_iteracoes, seed=SEED):\n",
    "        super().__init__(dataset, features, n_solucoes, n_iteracoes, seed)\n",
    "        self.nome_modelo = \"PSO-XGBoost\"\n",
    "        self.BOOSTERS = [\"gbtree\", \"gblinear\", \"dart\"]\n",
    "        self.run()\n",
    "\n",
    "    def run(self):\n",
    "        lower_bound = [10, 10, 0, 0, 0]\n",
    "        uppper_bound = [400, 400, 2, 1, 1]\n",
    "        bounds = (lower_bound, uppper_bound)\n",
    "\n",
    "        options = {'c1': 0.5, 'c2': 0.5, 'w': 0.5}\n",
    "        optimizer = GlobalBestPSO(n_particles=self.n_solucoes,\n",
    "                                  dimensions=5,\n",
    "                                  options=options,\n",
    "                                  bounds=bounds)\n",
    "\n",
    "        optimizer.optimize(self.get_fitness, iters=self.n_iteracoes)\n",
    "\n",
    "    def get_fitness(self, parts):\n",
    "        fit_lst = [self.run_objective_function(parts[j]) for j in range(self.n_solucoes)]\n",
    "\n",
    "        self.solucoes = sorted(self.solucoes, key=lambda a: a.fitness)\n",
    "        best = self.solucoes[0]\n",
    "        self.iteracoes.append(best)\n",
    "        self.salvar_csv()\n",
    "\n",
    "        return fit_lst\n",
    "\n",
    "    def run_objective_function(self, particle_arr):\n",
    "        solucao = SolucaoXGB()\n",
    "        solucao.estimators = int(particle_arr[0])\n",
    "        solucao.max_depth = int(particle_arr[1])\n",
    "        solucao.booster = self.BOOSTERS[int(particle_arr[2])]\n",
    "        solucao.reg_lambda = round(particle_arr[3], 4)\n",
    "        solucao.reg_alpha = round(particle_arr[4], 4)\n",
    "\n",
    "        search = list(filter(lambda par:\n",
    "                             par.estimators == solucao.estimators and\n",
    "                             par.max_depth == solucao.max_depth and\n",
    "                             par.booster == solucao.booster and\n",
    "                             par.reg_lambda == solucao.reg_lambda and\n",
    "                             par.reg_alpha == solucao.reg_alpha, self.solucoes))\n",
    "\n",
    "        if search:\n",
    "            self.solucoes.append(search[0])\n",
    "            return search[0].fitness\n",
    "\n",
    "        updater = \"coord_descent\" if solucao.booster == \"gblinear\" else None\n",
    "        modelo = XGBClassifier(random_state=self.seed,\n",
    "                               n_estimators=solucao.estimators,\n",
    "                               max_depth=solucao.max_depth,\n",
    "                               booster=solucao.booster,\n",
    "                               reg_lambda=solucao.reg_lambda,\n",
    "                               reg_alpha=solucao.reg_alpha,\n",
    "                               updater=updater)\n",
    "\n",
    "        solucao.fitness = self.objective_function(modelo, solucao)\n",
    "\n",
    "        self.solucoes.append(solucao)\n",
    "\n",
    "        return solucao.fitness\n"
   ],
   "id": "d98be8efeeb38eb",
   "outputs": [],
   "execution_count": 41
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Carregar Datasets",
   "id": "6b4216b7eb9fd06c"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:34.601913Z",
     "start_time": "2025-07-27T21:15:34.491095Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df_consumo = pd.read_csv(\"./dados/10_classes_normalizadas_lagadas.csv\", sep=';', decimal='.')\n",
    "df_features = pd.read_csv(\"resultados/features/fitness_features_regressao.csv\", sep=\";\", decimal=\".\")\n",
    "\n",
    "df_features = df_features.sort_values(\"RRMSE\").head(1).reset_index(drop=True)\n",
    "df_features = pd.DataFrame(\n",
    "    columns=str(df_features.iloc[0][\"FEATURES\"]).replace(\"(\", '').replace(\")\", '').replace(\"'\", \"\").split(\", \"))\n",
    "\n",
    "df_consumo = df_consumo.sort_values(\"CAMPUS\").sort_values(\"DATA\")\n",
    "df_features = df_features.columns\n",
    "df_features"
   ],
   "id": "4ee6eba5d31a7cc3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['TEMP_MÉD_ACC_MENS', 'TEMP_MIN_MAX_MENS', 'TEMP_MIN_ACC_MENS',\n",
       "       'PRECIPITAÇÃO_MIN_MENS', 'TEMP_MAX_ACC_MENS', 'PRECIPITAÇÃO_MAX_MENS',\n",
       "       'DIA_DA_SEMANA_sex', 'MÊS_abr', 'MÊS_jul', 'MÊS_mai', 'MÊS_mar',\n",
       "       'ANO_2024', 'ANO_2015', 'ANO_2017', 'ANO_2020', 'ANO_2014',\n",
       "       'CAMPUS_ARAPONGAS', 'CAMPUS_CAMPO LARGO', 'CAMPUS_COLOMBO',\n",
       "       'CAMPUS_LONDRINA - CENTRO', 'CAMPUS_LONDRINA - NORTE',\n",
       "       'CAMPUS_PARANAVAÍ', 'CAMPUS_QUEDAS DO IGUAÇU', 'CURSOS_GRAD_MATUTINO',\n",
       "       'CURSOS_GRAD_VESPERTINO', 'FÉRIAS', 'COVID', 'ORDEM', 'LAG_01',\n",
       "       'LAG_02', 'LAG_03', 'LAG_04', 'LAG_05', 'LAG_06', 'LAG_08', 'LAG_11',\n",
       "       'LAG_12'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 42
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "\n",
    "# Execução da Otimização"
   ],
   "id": "baa90686665cccff"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:17:40.933923Z",
     "start_time": "2025-07-27T21:17:32.778450Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Executa a otimização dos modelos normalmente, e reinicia a execução do código para o modelo LSTM - em função de estouro de memória.\n",
    "\n",
    "parser = argparse.ArgumentParser(description=\"Otimização de modelos.\")\n",
    "parser.add_argument('--s', default=SEEDS[0], help=\"Semente.\")\n",
    "parser.add_argument('--o', default=\"PSO\", help=\"Otimizador.\")\n",
    "parser.add_argument('--m', help=\"Modelo.\")\n",
    "parser.add_argument('--continua', default=\"1\",\n",
    "                    help=\"Continua a otimização do modelo com os demais otimizadores e sementes.\")\n",
    "args = parser.parse_args()\n",
    "\n",
    "if args.m is not None:\n",
    "    print(f\"Executando a otimização do modelo {args.m} com o otimizador {args.o} e semente {args.s}.\")\n",
    "    if args.o == \"PSO\":\n",
    "        if args.m == \"ESN\":\n",
    "            PSOESN(df_consumo, df_features, N_SOLUCOES, N_ITER, int(args.s))\n",
    "        elif args.m == \"LSTM\":\n",
    "            PSOLSTM(df_consumo, df_features, N_SOLUCOES, N_ITER, int(args.s))\n",
    "        elif args.m == \"MLP\":\n",
    "            PSOMLP(df_consumo, df_features, N_SOLUCOES, N_ITER, int(args.s))\n",
    "        elif args.m == \"RF\":\n",
    "            PSORF(df_consumo, df_features, N_SOLUCOES, N_ITER, int(args.s))\n",
    "        elif args.m == \"XGBoost\":\n",
    "            PSOXGB(df_consumo, df_features, N_SOLUCOES, N_ITER, int(args.s))\n",
    "        if args.continua == \"1\":\n",
    "            os.execv(sys.executable,\n",
    "                     [sys.executable,\n",
    "                      *sys.argv,\n",
    "                      '--s', str(args.s),\n",
    "                      '--o', 'SA',\n",
    "                      '--m', args.m,\n",
    "                      '--continua', args.continua])\n",
    "    elif args.o == \"SA\":\n",
    "        if args.m == \"ESN\":\n",
    "            SAESN(df_consumo, df_features, N_SOLUCOES, N_ITER, int(args.s))\n",
    "        elif args.m == \"LSTM\":\n",
    "            SALSTM(df_consumo, df_features, N_SOLUCOES, N_ITER, int(args.s))\n",
    "        elif args.m == \"MLP\":\n",
    "            SAMLP(df_consumo, df_features, N_SOLUCOES, N_ITER, int(args.s))\n",
    "        elif args.m == \"RF\":\n",
    "            SARF(df_consumo, df_features, N_SOLUCOES, N_ITER, int(args.s))\n",
    "        elif args.m == \"XGBoost\":\n",
    "            SAXGB(df_consumo, df_features, N_SOLUCOES, N_ITER, int(args.s))\n",
    "        if args.continua == \"1\" and SEEDS.index(int(args.s)) < len(SEEDS) - 1:\n",
    "            os.execv(sys.executable,\n",
    "                     [sys.executable, *sys.argv, '--s', str(SEEDS[SEEDS.index(int(args.s)) + 1]), '--o', 'PSO', '--m',\n",
    "                      args.m, '--continua', \"1\"])\n",
    "\n",
    "else:\n",
    "    print(\"Argumentos não informados. Executando a otimização dos modelos ESN, LSTM, MLP, RF e XGB.\")\n",
    "    for seed in SEEDS:\n",
    "        PSOESN(df_consumo, df_features, N_SOLUCOES, N_ITER, seed)\n",
    "        PSOMLP(df_consumo, df_features, N_SOLUCOES, N_ITER, seed)\n",
    "        PSORF(df_consumo, df_features, N_SOLUCOES, N_ITER, seed)\n",
    "        PSOXGB(df_consumo, df_features, N_SOLUCOES, N_ITER, seed)\n",
    "        SAESN(df_consumo, df_features, N_SOLUCOES, N_ITER, seed)\n",
    "        SAMLP(df_consumo, df_features, N_SOLUCOES, N_ITER, seed)\n",
    "        SARF(df_consumo, df_features, N_SOLUCOES, N_ITER, seed)\n",
    "        SAXGB(df_consumo, df_features, N_SOLUCOES, N_ITER, seed)\n",
    "    os.execv(sys.executable,\n",
    "             [sys.executable, *sys.argv, '--s', str(SEEDS[0]), '--o', 'PSO', '--m', 'LSTM', '--continua', \"1\"])\n"
   ],
   "id": "ad4bd534fadf80c5",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-27 18:17:32,801 - pyswarms.single.global_best - INFO - Optimize for 3 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.5}\n",
      "pyswarms.single.global_best:   0%|          |0/3"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n",
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "pyswarms.single.global_best:   0%|          |0/3"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shapes (373,10) and (1,) not aligned: 10 (dim 1) != 1 (dim 0)\n",
      "{'Reservoirs': 373, 'Sparsity': 0.3329, 'Spectral Radius': 0.0242, 'Fitness': None}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "KeyboardInterrupt\n",
      "\n"
     ]
    }
   ],
   "execution_count": 52
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# Optimization Results\n",
    "## Fitness Evolution"
   ],
   "id": "c94b4e8c997228e6"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:43.214192Z",
     "start_time": "2025-07-27T21:15:41.738867Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def formatar_y(valor, pos):\n",
    "    if valor >= 1:\n",
    "        return f\"{valor:.2f}+\"\n",
    "    return f\"{valor:.2f}\"\n",
    "\n",
    "\n",
    "for modelo in MODELOS:\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    plt.rcParams['xtick.labelsize'] = 18\n",
    "    plt.rcParams['ytick.labelsize'] = 18\n",
    "    plt.rcParams.update({'font.size': 18})\n",
    "    plt.rcParams['axes.prop_cycle'] = plt.cycler(\n",
    "        color=[\"blue\", \"red\"])\n",
    "\n",
    "    for otimizador in OTIMIZADORES:\n",
    "        df = pd.DataFrame()\n",
    "        final_fitness = {}\n",
    "        for seed in SEEDS:\n",
    "            try:\n",
    "                novo_df = pd.read_csv(f'resultados/otimização - classificação/{otimizador}-{modelo} SEED {seed}.csv',\n",
    "                                      sep=\";\",\n",
    "                                      decimal=\".\", header=0)\n",
    "            except Exception as e:\n",
    "                continue\n",
    "\n",
    "            if otimizador == \"PSO\":\n",
    "                df[seed] = pd.concat([novo_df[\"Fitness\"].to_frame()] * N_ITER, ignore_index=True)\n",
    "                df[seed] = sorted(df[seed].values, reverse=True)\n",
    "            else:\n",
    "                df[seed] = novo_df[\"Fitness\"]\n",
    "            df[seed] = np.where(df[seed] > 1, 1, df[seed])  # Limitar valores de Fitness a 1\n",
    "            final_fitness[seed] = novo_df[\"Fitness\"].iloc[-1]  # Último valor de Fitness para cada seed\n",
    "\n",
    "        df = np.round(df, decimals=2)\n",
    "        best_seed = min(final_fitness, key=final_fitness.get)\n",
    "        plt.plot(range(1, len(df) + 1), [x for x in df[best_seed]], label=f\"{otimizador}-{modelo}\")\n",
    "\n",
    "        plt.xlabel('Avaliações da FO')\n",
    "        plt.ylabel('RRMSE')\n",
    "        plt.gca().yaxis.set_major_formatter(FuncFormatter(formatar_y))\n",
    "        plt.xlim(0, N_ITER * N_SOLUCOES)\n",
    "        ax = plt.gca()\n",
    "        ax.set_facecolor('white')\n",
    "        plt.grid(True, color='grey', linestyle=\"--\", linewidth=0.5)\n",
    "        plt.legend(facecolor='white')\n",
    "        plt.savefig(f\"./resultados/otimização - classificação/{modelo}.png\", bbox_inches='tight')\n",
    "        plt.show()"
   ],
   "id": "6f002bb1d7ed628",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 44
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Best Params",
   "id": "624dff67cf716d63"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:43.616447Z",
     "start_time": "2025-07-27T21:15:43.283914Z"
    }
   },
   "cell_type": "code",
   "source": [
    "best = {}\n",
    "for modelo in [\"ESN\", \"LSTM\", \"MLP\", \"RF\", \"XGBoost\"]:\n",
    "    df = pd.DataFrame()\n",
    "    for otimizador in [\"PSO\", \"SA\"]:\n",
    "        for seed in SEEDS:\n",
    "            try:\n",
    "                novo_df = pd.read_csv(f'resultados/otimização - classificação/{otimizador}-{modelo} SEED {seed}.csv',\n",
    "                                      sep=\";\", decimal=\".\",\n",
    "                                      header=0)\n",
    "                df = pd.concat([df, novo_df], axis=0)\n",
    "            except Exception as e:\n",
    "                continue\n",
    "\n",
    "    df = df.sort_values(by=[\"Fitness\"])\n",
    "    df[df.isnull()] = None\n",
    "    best[f\"{modelo}\"] = df[:1]\n",
    "\n"
   ],
   "id": "f735813030a6bb7b",
   "outputs": [],
   "execution_count": 45
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### ESN",
   "id": "6c761967e551e852"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:43.683763Z",
     "start_time": "2025-07-27T21:15:43.676217Z"
    }
   },
   "cell_type": "code",
   "source": "best[\"ESN\"].transpose()",
   "id": "55946f504a5a055a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                        171\n",
       "Unnamed: 0       171.000000\n",
       "Reservoirs        14.000000\n",
       "Sparsity           0.414000\n",
       "Spectral Radius    0.353000\n",
       "Fitness            0.320256"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>171</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>171.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reservoirs</th>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparsity</th>\n",
       "      <td>0.414000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spectral Radius</th>\n",
       "      <td>0.353000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fitness</th>\n",
       "      <td>0.320256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 46
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### LSTM",
   "id": "bfaa066a29cd62b2"
  },
  {
   "metadata": {
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     "end_time": "2025-07-27T21:15:43.782406Z",
     "start_time": "2025-07-27T21:15:43.776317Z"
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   },
   "cell_type": "code",
   "source": "best[\"LSTM\"].transpose()",
   "id": "70e62db65bfa7bc9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                 160\n",
       "Unnamed: 0       160\n",
       "Units            368\n",
       "Epochs           178\n",
       "Batch Size        30\n",
       "Activation   sigmoid\n",
       "Fitness     0.343295"
      ],
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       "      <th>160</th>\n",
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       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epochs</th>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Batch Size</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Activation</th>\n",
       "      <td>sigmoid</td>\n",
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       "    <tr>\n",
       "      <th>Fitness</th>\n",
       "      <td>0.343295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "execution_count": 47
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### MLP",
   "id": "e0e573bf6e2a4c74"
  },
  {
   "metadata": {
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     "end_time": "2025-07-27T21:15:43.823607Z",
     "start_time": "2025-07-27T21:15:43.802607Z"
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   },
   "cell_type": "code",
   "source": "best[\"MLP\"].transpose()",
   "id": "fb4e3d17337f74a1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                      0\n",
       "Unnamed: 0            0\n",
       "Hidden Layers       151\n",
       "Alpha          0.035106\n",
       "Activation         relu\n",
       "Fitness       -0.104167"
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       "      <td>0.035106</td>\n",
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       "      <th>Fitness</th>\n",
       "      <td>-0.104167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "execution_count": 48
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### RF",
   "id": "28f5dd650e911d15"
  },
  {
   "metadata": {
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     "end_time": "2025-07-27T21:15:44.029536Z",
     "start_time": "2025-07-27T21:15:44.022829Z"
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   },
   "cell_type": "code",
   "source": "best[\"RF\"].transpose()",
   "id": "576650034834c9c3",
   "outputs": [
    {
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      "text/plain": [
       "                           14\n",
       "Unnamed: 0          14.000000\n",
       "N_estimators        21.000000\n",
       "Max_depth          179.000000\n",
       "Min_samples_split   28.000000\n",
       "Min_samples_leaf     5.000000\n",
       "Fitness              0.366958"
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       "      <th>14</th>\n",
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       "    <tr>\n",
       "      <th>Max_depth</th>\n",
       "      <td>179.000000</td>\n",
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       "    <tr>\n",
       "      <th>Min_samples_split</th>\n",
       "      <td>28.000000</td>\n",
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       "      <th>Min_samples_leaf</th>\n",
       "      <td>5.000000</td>\n",
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       "    <tr>\n",
       "      <th>Fitness</th>\n",
       "      <td>0.366958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "execution_count": 49
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### XGB",
   "id": "3f65956442f9bc50"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T21:15:44.132756Z",
     "start_time": "2025-07-27T21:15:44.126170Z"
    }
   },
   "cell_type": "code",
   "source": "best[\"XGBoost\"].transpose()",
   "id": "1bc362fa4f3b263e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                   156\n",
       "Unnamed: 0         156\n",
       "N_estimators       366\n",
       "Max_depth          177\n",
       "Booster         gbtree\n",
       "Lambda           0.285\n",
       "Alpha              0.1\n",
       "Fitness       0.367669"
      ],
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       "      <td>177</td>\n",
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       "      <td>0.285</td>\n",
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       "      <th>Alpha</th>\n",
       "      <td>0.1</td>\n",
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       "      <th>Fitness</th>\n",
       "      <td>0.367669</td>\n",
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     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "execution_count": 50
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